Liang Zhang

Assistant Professor

University of Arizona

United States

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About

Liang Zhang is an Assistant Professor at the University of Arizona, specializing in building energy analysis and modeling. His recent research encompasses the application of large language models in the building energy sector, including automated feature extraction and energy consumption prediction. With a focus on enhancing energy modeling through innovative methodologies, he explores the intersection of artificial intelligence and energy efficiency.

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UNITED STATES

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Liang Zhang

Research Interests

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Articles (21)

How Commute Time and EV Ownership Shape Residential Cooling Energy Load Profiles

Prompt engineering to inform large language model in automated building energy modeling

Large language models for building energy applications: Opportunities and challenges

Large language model-based agent Schema and library for automated building energy analysis and modeling

Performance evaluation of automated data-driven feature extraction and selection methods for practical and scalable building energy consumption prediction models

Opportunities of applying Large Language Models in building energy sector

Automatic building energy model development and debugging using large language models agentic workflow

Physics-informed hybrid modeling methodology for building infiltration

EPlus-LLM: A large language model-based computing platform for automated building energy modeling

Large language model-based interpretable machine learning control in building energy systems

Advancing building energy modeling with large language models: Exploration and case studies

Challenges and opportunities of machine learning control in building operations

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

Active learning strategy for high fidelity short-term data-driven building energy forecasting

Data-driven building energy modeling with feature selection and active learning for data predictive control

High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling

Sensor impacts on building and HVAC controls: A critical review for building energy performance

Grey-box modeling and application for building energy simulations - A critical review

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Collaborators (6)

Xinghua Gao

Jin Wen

Alyssa Ryan

Zheng O'Neill

Ralph Muehleisen

Zhihao Ma

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